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With increased degradation of natural resources due to land use decisions and the subsequent loss of biodiversity across large spatial scales, there is a need for a Spatial Decision Support System (SDSS) which showcases the impacts of developments on terrestrial and aquatic ecosystems. The Gap Analysis Program (GAP) and a Bayesian Belief Network (BBN) were used to assess the impacts of an impoundment in the Bienville National Forest, Smith County, Mississippi on landcovers, threatened and endangered species, species richness and fish populations. A test impoundment site was chosen on Ichusa Creek and using GAP data, landcovers, species and species richness were compared with those of Bienville National Forest, Smith County, Mississippi. For the aquatic analysis, a BBN model was developed for each fish so that population probabilities could be calculated using a given configuration of available habitats and compared to current fish population.

Commodity brain-computer interfaces (BCI) are beginning to accompany everything from toys and games to sophisticated health care devices. These contemporary interfaces allow for varying levels of interaction with a computer. Not surprisingly, the more intimately BCIs are integrated into the nervous system, the better the control a user can exert on a system. At one end of the spectrum, implanted systems can enable an individual with full body paralysis to utilize a robot arm and hold hands with their loved ones [28, 62]. On the other end of the spectrum, the untapped potential of commodity devices supporting electroencephalography (EEG) and electromyography (EMG) technologies require innovative approaches and further research. This thesis proposes a modularized software architecture designed to build flexible systems based on input from commodity BCI devices. An exploratory study using a commodity EEG provides concrete assessment of the potential for the modularity of the system to foster innovation and exploration, allowing for a combination of a variety of algorithms for manipulating data and classifying results. Specifically, this study analyzes a pipelined architecture for researchers, starting with the collection of spatio temporal brain data (STBD) from a commodity EEG device and correlating it with intentional behaviour involving keyboard and mouse input. Though classification proves troublesome in the preliminary dataset considered, the architecture demonstrates a unique and flexible combination of a liquid state machine (LSM) and a deep belief network (DBN). Research in methodologies and techniques such as these are required for innovation in BCIs, as commodity devices, processing power, and algorithms continue to improve. Limitations in terms of types of classifiers, their range of expected inputs, discrete versus continuous data, spatial and temporal considerations and alignment with neural networks are also identified. / Graduate / 0317 / 0984 / jasoncummer@gmail.com

Decisions of adopting best management practices made on residential properties play an important role in reduction of nutrient loading from non-point sources into Lake Champlain and other waterbodies in Vermont. In this study, we use Bayesian belief network (BBN) to analyze a 2015 survey dataset about adoption of six types of green infrastructures (GSIs) in Vermont’s residential areas. Learning BBNs from physical probabilities of the variables provides a visually explicit approach to reveal the message delivered by the dataset. Using both unsupervised and supervised machine learning algorithms, we are able to generate networks that connect the variables of interest and conduct inference to look into the probabilistic associations between the variables. Unsupervised learning reveals the underlying structures of the dataset without presumptions. Supervised learning provides insights for how each factor (e.g. demographics, risk perception, and attribution of responsibilities) influence individuals’ pro-environmental behaviors. We also compare the effectiveness of BBN approach and logistic regression in predicting the pro-environmental behaviors (adoption of GSIs). The results show that influencing factors for current adoption vary by different types of GSI. Risk perception of stormwater issues are associated with adoption of GSIs. Runoff issues are more likely to be considered as the governments’ (town, state, and federal agencies) responsibility, whereas lawn erosion is more likely to be considered as the residents’ own responsibility. When using the same set of variables to predict pro-environmental behaviors (adoption of GSI), BBN approach produces more accurate prediction compared to logistic regression.

Project risk assessment is an effective tool for planning and controlling cost, time and achieving the technical performance of a building construction project. Construction projects often face a lot of uncertainties, which places building construction projects at the risk of cost, time overruns as well as poor quality delivery. Considering the limited resources of developing countries, there is need to complete building projects on-time, on-budget, and to meet optimal quality hence, risk management is an important part of the decision making process in construction industry as it determines the success or failure of construction projects. In line with this need, this research aims to establish a system to improve the time, cost and quality performance of building construction projects in developing countries, through a comprehensive risk management model that ensures the expectations of clients are met. To achieve the aim of this research, a mixed methodological approach was adopted. Through the review of literature, a conceptual risk management framework suitable to elaborate risk assessment of building construction projects especially for developing countries was developed. A questionnaire survey using a nonprobability sampling technique was conducted to elicit information from construction professionals in Nigeria to assess their perception of 79 risk factors identified from literature review based on the likelihood of occurrence and impact on projects using a five point scale. Responses from 343 construction professionals were drawn from 305 contractors and subcontractors and 38 clients (private and public) within the Nigerian construction sector. Response data was subjected to descriptive statistics to depict the frequency distribution and central tendency of responses. Subsequently, the risk acceptability matrix (RAM) was adopted to categorise and prioritise risk factors. 27 critical risks that affect building construction projects were identified. A Bayesian Belief Network (BBN) model was developed by structural learning and used to examine the cause and effect relationship amongst the 27 critical risk factors. The developed BBN model was subjected to validation using a multiple case study of two building construction projects in Nigeria. The result showed the interrelation between the 27 risk factors and how they contributed to cost and time overruns as well as quality problems. The critical risks directly affecting the cost of building construction project were: fluctuation of material prices; health and safety issues; bribery and corruption; material wastage; poor site management and supervision; and time overruns. The critical factors identified to directly affect quality were: supply of defective materials; working under harsh conditions; improper construction methods; lack of protective equipment; ineffective time allocation; poor communication between involved stakeholders; and unsuitable leadership style. Time overruns on building construction projects was directly caused by: quality problems; low productivity; improper construction methods; poor communication between involved parties; delayed payments in contracts; and poor site management and supervision. As a consolidation of the findings of this research, a BBN model for identifying risk factors that directly affect time, cost and quality on building construction projects has been developed which has the potential for assisting construction stake holders to manage risks on their projects. In view of the findings, a best practice system for risk management in building construction projects in Nigeria has been developed with an implementation guide to help building construction practitioners to successfully implement risk management on their building construction projects. Suitable risk responses, also in the form of recommendations have been identified. The strategies include actions to be taken to respond to risks based on their perceived significance or acceptability as well as some positive risk responses, such as exploiting, sharing, enhancing and accepting, and other negative risk responses, such as avoidance, mitigation transfer and acceptance.

Yes / The most recent face recognition systems are mainly dependent on feature representations obtained using either local handcrafted-descriptors, such as local binary patterns (LBP), or use a deep learning approach, such as deep belief network (DBN). However, the former usually suffers from the wide variations in face images, while the latter usually discards the local facial features, which are proven to be important for face recognition. In this paper, a novel framework based on merging the advantages of the local handcrafted feature descriptors with the DBN is proposed to address the face recognition problem in unconstrained conditions. Firstly, a novel multimodal local feature extraction approach based on merging the advantages of the Curvelet transform with Fractal dimension is proposed and termed the Curvelet–Fractal approach. The main motivation of this approach is that theCurvelet transform, a newanisotropic and multidirectional transform, can efficiently represent themain structure of the face (e.g., edges and curves), while the Fractal dimension is one of the most powerful texture descriptors for face images. Secondly, a novel framework is proposed, termed the multimodal deep face recognition (MDFR)framework, to add feature representations by training aDBNon top of the local feature representations instead of the pixel intensity representations. We demonstrate that representations acquired by the proposed MDFR framework are complementary to those acquired by the Curvelet–Fractal approach. Finally, the performance of the proposed approaches has been evaluated by conducting a number of extensive experiments on four large-scale face datasets: the SDUMLA-HMT, FERET, CAS-PEAL-R1, and LFW databases. The results obtained from the proposed approaches outperform other state-of-the-art of approaches (e.g., LBP, DBN, WPCA) by achieving new state-of-the-art results on all the employed datasets.

Approved for public release; distribution in unlimited. / The principles of Sun Tzu's Art of War have been widely used by business executives and military officers with much success in the realm of competition and conflict. However, when conflict situations arise in a highly stressful environment coupled with the pressure of time, decision makers may not be able to consider all the key concepts when forming their decisions or strategies. Therefore, a structured reasoning approach may be used to apply Sun Tzu's principles correctly and fully. Sun Tzu's principles are believed to be able to be modeled mathematically; hence, a Bayesian Network model (a form of mathematical tool using probability theory) is used to capture Sun Tzu's principles and provide the structured reasoning approach. Scholars have identified incompleteness in Sun Tzu's appreciation of information in war and his application of secret agents. This incompleteness resulted in circular reasoning when both sides of the conflict apply his principles. This circular reasoning can be resolved through the use of advanced probability theory. A Bayesian Network Model however, not only provides a structured reasoning approach, but more importantly, it can also resolve the circular reasoning problem that has been identified. / Captain, Singapore Army

Le risque de chute provoqué par le phénomène épisodique de ‘Freeze of Gait’ (FoG) est un symptôme commun de la maladie de Parkinson. Cette étude concerne la détection et le diagnostic des épisodes de FoG à l'aide d'un prototype multi-capteurs. La première contribution est l'introduction de nouveaux capteurs (télémètres et goniomètres) dans le dispositif de mesure pour la détection des épisodes de FoG. Nous montrons que l'information supplémentaire obtenue avec ces capteurs améliore les performances de la détection. La seconde contribution met œuvre un algorithme de détection basé sur des réseaux de neurones gaussiens. Les performance de cet algorithme sont discutées et comparées à l'état de l'art. La troisième contribution est développement d'une approche de modélisation probabiliste basée sur les réseaux bayésiens pour diagnostiquer le changement du comportement de marche des patients avant, pendant et après un épisode de FoG. La dernière contribution est l'utilisation de réseaux bayésiens arborescents pour construire un modèle global qui lie plusieurs symptômes de la maladie de Parkinson : les épisodes de FoG, la déformation de l'écriture et de la parole. Pour tester et valider cette étude, des données cliniques ont été obtenues pour des patients atteints de Parkinson. Les performances en détection, classification et diagnostic sont soigneusement étudiées et évaluées. / Freezing of Gait (FoG) is an episodic phenomenon that is a common symptom of Parkinson's disease (PD). This research is headed toward implementing a detection, diagnosis and correction system that prevents FoG episodes using a multi-sensor device. This particular study aims to detect/diagnose FoG using different machine learning approaches. In this study we validate the choice of integrating multiple sensors to detect FoG with better performance. Our first level of contribution is introducing new types of sensors for the detection of FoG (telemeter and goniometer). An advantage in our work is that due to the inconsistency of FoG events, the extracted features from all sensors are combined using the Principal Component Analysis technique. The second level of contribution is implementing a new detection algorithm in the field of FoG detection, which is the Gaussian Neural Network algorithm. The third level of contribution is developing a probabilistic modeling approach based on Bayesian Belief Networks that is able to diagnosis the behavioral walking change of patients before, during and after a freezing event. Our final level of contribution is utilizing tree-structured Bayesian Networks to build a global model that links and diagnoses multiple Parkinson's disease symptoms such as FoG, handwriting, and speech. To achieve our goals, clinical data are acquired from patients diagnosed with PD. The acquired data are subjected to effective time and frequency feature extraction then introduced to the different detection/diagnosis approaches. The used detection methods are able to detect 100% of the present appearances of FoG episodes. The classification performances of our approaches are studied thoroughly and the accuracy of all methodologies is considered carefully and evaluated

This Ph.D thesis work is dedicated to automatic facial analysis in 3D, including facial landmarking and facial expression recognition. Indeed, facial expression plays an important role both in verbal and non verbal communication, and in expressing emotions. Thus, automatic facial expression recognition has various purposes and applications and particularly is at the heart of "intelligent" human-centered human/computer(robot) interfaces. Meanwhile, automatic landmarking provides aprior knowledge on location of face landmarks, which is required by many face analysis methods such as face segmentation and feature extraction used for instance for expression recognition. The purpose of this thesis is thus to elaborate 3D landmarking and facial expression recognition approaches for finally proposing an automatic facial activity (facial expression and action unit) recognition solution.In this work, we have proposed a Bayesian Belief Network (BBN) for recognizing facial activities, such as facial expressions and facial action units. A StatisticalFacial feAture Model (SFAM) has also been designed to first automatically locateface landmarks so that a fully automatic facial expression recognition system can be formed by combining the SFAM and the BBN. The key contributions are the followings. First, we have proposed to build a morphable partial face model, named SFAM, based on Principle Component Analysis. This model allows to learn boththe global variations in face landmark configuration and the local ones in terms of texture and local geometry around each landmark. Various partial face instances can be generated from SFAM by varying model parameters. Secondly, we have developed a landmarking algorithm based on the minimization an objective function describing the correlation between model instances and query faces. Thirdly, we have designed a Bayesian Belief Network with a structure describing the casual relationships among subjects, expressions and facial features. Facial expression oraction units are modelled as the states of the expression node and are recognized by identifying the maximum of beliefs of all states. We have also proposed a novel method for BBN parameter inference using a statistical feature model that can beconsidered as an extension of SFAM. Finally, in order to enrich information usedfor 3D face analysis, and particularly 3D facial expression recognition, we have also elaborated a 3D face feature, named SGAND, to characterize the geometry property of a point on 3D face mesh using its surrounding points.The effectiveness of all these methods has been evaluated on FRGC, BU3DFEand Bosphorus datasets for facial landmarking as well as BU3DFE and Bosphorus datasets for facial activity (expression and action unit) recognition.

This thesis presents an evaluation of the effectiveness of an agroforestry extension program to smallholder farmers on Leyte Island, the Philippines. The imperative for reforestation is well recognised in the Philippines and was the impetus for this program which provided farmers with assistance to establish and silviculturally manage timber trees on their land. Because the cost-effectiveness of agroforestry extension is increased if farmers develop self-efficacy without extensive training, the extension program was offered in two regimes to test the necessity for extended assistance. In the extended assistance regime, farmers were offered on-site assistance to collect seed, grow seedlings, prepare sites and establish trees, whereas in the limited assistance regime, farmers were only offered assistance to collect seed and grow seedlings. Descriptive statistics were collected of farmers’ acceptance of technology and the manner in which technology was adapted to suit their personal circumstances. Translated conversations between farmers and extension staff also provided a rich source of data which provided insights into farmers’ motivation. Extension activities were reviewed at a mid-program workshop, a final on-site inspection and an end-of-program workshop. Farmers responded positively to the extended assistance program which helped them to grow and out-plant seedlings. The limited assistance program was relatively unsuccessful. Overall, the extension program was successful in shifting the initiative for further planting from extension staff to participating farmers. However, farmers showed little interest in applying silvicultural thinning or pruning to existing plantations of trees because extension advice was not congruent with their existing mental models of these procedures. Systems modelling of socio-economic variables which had been found to affect program outcomes was used to predict critical success factors. A key constraint to program recruitment was found to be farmers’ perception of harvest security, even when their needs for technology and planting materials are met. Modelling also cast doubt on the usefulness of written extension materials and emphasised the necessity for extended face-to-face technical assistance. Although conducted in Leyte, the findings of this research provide guidance for issues which affect the adoption of agroforestry both in the Philippines and in other countries. The research found that it was possible to recruit and motivate farmers without providing material incentives. If farmers experienced unexpected problems, providing extended face-to-face contact and assistance was critical if catastrophic losses of participating farmers were to be avoided. The failure of attempts to introduce advanced-age silviculture also indicated a need to elicit farmers’ mental models as a precursor or parallel enquiry to extension activities. In a situation where little was initially known about farmers’ understanding of agroforestry technology or the variables which affect their acceptance or rejection of extension assistance, the results of this research have shown that it is possible to build the capacity of farmers to establish timber trees. This result is in contrast to the acknowledged failure of the logging concession system in the Philippines and the difficulties faced by some industrial plantations and community-based programs. This investigation has shown that an opportunity exists to lift the level of tree planting in Leyte, provided that system variables which are either critical success factors or impediments are addressed.